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Definition, approaches, and analysis of code duplication detection (2006-2020): a critical review

NEURAL COMPUTING & APPLICATIONS 2022年第23期34卷 20507-20537页

作者： Chen, Chang-Feng Zain, Azlan Mohd Zhou, Kai-Qing Univ Teknol Malaysia Fac Engn Sch Comp Skudai 80310 Johor Malaysia Univ Teknol Malaysia Big Data Ctr Skudai 80310 Johor Malaysia Jishou Univ Coll Informat Sci & Engn Jishou 416000 Peoples R China

code duplication detection is the act of finding similar code in software development. It is important for software engineer to address the issues of code duplication detection. In this paper, a critical review of previous works on code duplication for code clone and plagiarism detection is performed. The review involves five main parts. Firstly, a systematic literature review is conducted to confirm the selected articles. Secondly, a critical review of different code duplication approaches is conducted based on three phases;processing, detection, and decision. Thirdly, statistical analysis of the number of review articles is performed to show the trends and hots of code duplication research. Moreover, quantitative analysis of different code duplication approaches is presented to show the effectiveness of different approaches. Fourthly, the advantages and disadvantages of different approaches and techniques are summarized and discussed. Finally, the conclusion of the review is summarized and future research direction of code duplication is described.

关键词： code duplication code duplication detection System literature review

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Analysis of Source code duplication in Ethreum Smart Contracts 28

Analysis of Source Code Duplication in Ethreum Smart Contrac...

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28th IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER)

作者： Pierro, Giuseppe Antonio Tonelli, Roberto Ctr Inria Lille Nord Europe Lille France Univ Cagliari Dept Math & Comp Sci Cagliari Italy

ISBN: (纸本)9781728196305

The practice of writing smart contracts for the Ethereum blockchain is quite recent and still in development. A blockchain developer should expect constant changes in the security software field, as new bugs and security risks are discovered, and new good practices are developed. Following the security practices accepted in the blockchain community is not enough to ensure the writing of secure smart contracts. The paper aims to study the practice of code cloning among the smart contracts by analyzing two corpora. The first corpus, the "Smart-Corpus", includes smart contracts already deployed in the Ethereum blockchain. The second corpus, the "OpenZeppelin's Solidity Library", is supervised by a community of developers who constantly take care to increase the security and efficiency of the smart contracts included in the corpus. From the comparative analysis of the corpora, we observe that the smart contracts developers frequently duplicate the code by cloning already existing smart contracts which are not part of the "OpenZeppelin corpus". In particular, we found that 79.1% of smart contracts contain duplicated code and only 18.4% of smart contracts reuse the code by implementing a smart corpus belonging to the OpenZeppelin repository. The paper discusses the advantages and the disadvantages of code duplication in the Ethereum blockchain ecosystem, and suggests to refer to the smart contracts of the OpenZeppelin's Solidity Library. The Ethereum blockchain community can indeed benefit from using the tested code presented in OpenZeppelin's Solidity Library to increase its security.

关键词： code duplication smart contract Ethereum blockchain

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code duplication and Reuse in Jupyter Notebooks

Code Duplication and Reuse in Jupyter Notebooks

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IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

作者： Koenzen, Andreas P. Ernst, Neil A. Storey, Margaret-Anne D. Univ Victoria Dept Comp Sci Victoria BC Canada

ISBN: (纸本)9781728169019

Duplicating one's own code makes it faster to write software. This expediency is particularly valuable for users of computational notebooks. duplication allows notebook users to quickly test hypotheses and iterate over data. In this paper, we explore how much, how and from where code duplication occurs in computational notebooks, and identify potential barriers to code reuse. Previous work in the area of computational notebooks describes developers' motivations for reuse and duplication but does not show how much reuse occurs or which barriers they face when reusing code. To address this gap, we first analyzed GitHub repositories for code duplicates contained in a repository's Jupyter notebooks, and then conducted an observational user study of code reuse, where participants solved specific tasks using notebooks. Our findings reveal that repositories in our sample have a mean self-duplication rate of 7.6%. However, in our user study, few participants duplicated their own code, preferring to reuse code from online sources.

关键词： Jupyter computational notebooks code duplication code clones code reuse data analysis data exploration exploratory programming

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code duplication on Stack Overflow 42

Code Duplication on Stack Overflow

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42nd IEEE/ACM International Conference on Software Engineering: New Ideas and Emerging Results (ICSE-NIER)

作者： Baltes, Sebastian Treude, Christoph Univ Adelaide Adelaide SA Australia

ISBN: (纸本)9781450371261

Despite the unarguable importance of Stack Overflow (SO) for the daily work of many software developers and despite existing knowledge about the impact of code duplication on software maintainability, the prevalence and implications of code clones on SO have not yet received the attention they deserve. In this paper, we motivate why studies on code duplication within SO are needed and how existing studies on code reuse differ from this new research direction. We present similarities and differences between code clones in general and code clones on SO and point to open questions that need to be addressed to be able to make data-informed decisions about how to properly handle clones on this important platform. We present results from a first preliminary investigation, indicating that clones on SO are common and diverse. We further point to specific challenges, including incentives for users to clone successful answers and difficulties with bulk edits on the platform, and conclude with possible directions for future work.

关键词： code duplication code clones software maintenance software evolution software licenses stack overflow

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Refactoring cross-project code duplication in an industrial software product line: A case study from RDW

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Journal of Systems and Software 2025年 230卷

作者： Jesper van der Zwaag Frank Driesens Bouwe Postma Andrea Capiluppi Department of Computing Science University of Groningen The Netherlands RDW Groningen The Netherlands

code duplication across projects is a common challenge in Software Product Line (SPL) development, impacting maintainability and efficiency. This paper reports on an applied research project conducted within RDW, The Netherlands Vehicle Authority, aimed at addressing cross-project code duplication in a multi-application SPL environment. The objective of this work was to investigate the impact of refactoring cross-project duplicated code on maintainability, code reuse, and technical debt within RDW’s Electronic Services System (EDS), composed of 62 applications. We used an action research approach, focusing on two applications to identify, refactor, and package duplicated features into reusable components. SonarQube was used to detect and quantify duplication, and the effectiveness of refactoring was evaluated using code duplication reduction metrics and developer feedback. The refactoring effort led to the removal of 38% of the duplicated code on average, improving code reuse by 33%. Efficiency gains were observed in the second application due to the experience from the first intervention, reducing the time required for code maintenance and updates by 89%. While the study demonstrates that systematically addressing cross-project code duplication in SPLs improves maintainability and reduces technical debt, it also highlights a key limitation: refactoring efforts need to be undertaken early to be feasible. As the number of applications in RDW’s system steadily expanded to 80, the engineers reported that the required refactoring work, although highly beneficial, has become too extensive and time-consuming to be practical. The effort was ultimately left too late, making large-scale refactoring no longer a viable solution. Future strategies should focus on early duplication identification, architectural solutions such as modularization or microservices, and automated dependency management to prevent similar challenges from arising.

关键词： code duplication Action research Software product lines Refactoring Maintainability Reusability

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Dominance-Based duplication Simulation (DBDS) code duplication to Enable Compiler Optimizations 2018

Dominance-Based Duplication Simulation (DBDS) Code Duplicati...

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16th International Symposium on code Generation and Optimization (CGO)

作者： Leopoldseder, David Stadler, Lukas Wurthinger, Thomas Eisl, Josef Simon, Doug Moessenboeck, Hanspeter Johannes Kepler Univ Linz Linz Austria Oracle Labs Linz Austria Oracle Labs Zurich Switzerland

ISBN: (纸本)9781450356176

Compilers perform a variety of advanced optimizations to improve the quality of the generated machine code. However, optimizations that depend on the data flow of a program are often limited by control-flow merges. code duplication can solve this problem by hoisting, i.e. duplicating, instructions from merge blocks to their predecessors. However, finding optimization opportunities enabled by duplication is a non-trivial task that requires compile-time intensive analysis. This imposes a challenge on modern (just-in-time) compilers: Duplicating instructions tentatively at every control flow merge is not feasible because excessive duplication leads to uncontrolled code growth and compile time increases. Therefore, compilers need to find out whether a duplication is beneficial enough to be performed. This paper proposes a novel approach to determine which duplication operations should be performed to increase performance. The approach is based on a duplication simulation that enables a compiler to evaluate different success metrics per potential duplication. Using this information, the compiler can then select the most promising candidates for optimization. We show how to map duplication candidates into an optimization cost model that allows us to trade-off between different success metrics including peak performance, code size and compile time. We implemented the approach on top of the GraalVM and evaluated it with the benchmarks Java DaCapo, Scala DaCapo, JavaScript Octane and a micro-benchmark suite, in terms of performance, compilation time and code size increase. We show that our optimization can reach peak performance improvements of up to 40% with a mean peak performance increase of 5.89%, while it generates a mean code size increase of 9.93% and mean compile time increase of 18.44%.

关键词： code duplication Tail duplication Compiler Optimizations Just-In-Time Compilation Virtual Machines

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Using Machine Learning to Predict the code Size Impact of duplication Heuristics in a Dynamic Compiler 18

Using Machine Learning to Predict the Code Size Impact of Du...

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18th ACM SIGPLAN International Conference on Managed Programming Languages and Runtimes (MPLR)

作者： Mosaner, Raphael Leopoldseder, David Stadler, Lukas Moessenboeck, Hanspeter Johannes Kepler Univ Linz Linz Austria Oracle Labs Vienna Austria Oracle Labs Linz Austria

ISBN: (纸本)9781450386753

code duplication is a major opportunity to enable optimizations in subsequent compiler phases. However, duplicating code prematurely or too liberally can result in tremendous code size increases. Thus, modern compilers use trade-offs between estimated costs in terms of code size increase and benefits in terms of performance increase. In the context of this ongoing research project, we propose the use of machine learning to provide trade-off functions with accurate predictions for code size impact. To evaluate our approach, we implemented a neural network predictor in the GraalVM compiler and compared its performance against a human-crafted, highly tuned heuristic. First results show promising performance improvements, leading to code size reductions of more than 10% for several benchmarks. Additionally, we present an assistance mode for finding flaws in the human-crafted heuristic, leading to improvements for the duplication optimization itself.

关键词： code duplication Machine Learning Neural Networks Regression Dynamic Compiler Optimization Heuristics

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code Clone Detection Techniques Based on Large Language Models

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IEEE ACCESS 2025年 13卷 46136-46146页

作者： Almatrafi, Afnan A. Eassa, Fathy A. Sharaf, Sanaa A. King Abdulaziz Univ Fac Comp & Informat Technol Comp Sci Dept Jeddah 21589 Saudi Arabia Umm Al Qura Univ Jamoum Univ Coll Comp Dept Mecca 21955 Saudi Arabia

code duplication, commonly known as code cloning, is a persistent challenge in software development. While reusing code fragments boosts productivity, excessive cloning poses challenges to maintenance and elevates the risk of bugs. Therefore, integrating code clone detection into the development process is crucial. The extensive code-related knowledge inherent in Large Language Models (LLMs) renders them high-potential candidates for addressing diverse software engineering challenges. However, the effectiveness of LLMs in the specific task of code clone detection requires precise evaluation. This paper proposes an innovative methodology leveraging few-shot instruction-tuned GPT-3.5 Turbo and GPT-4 to detect code clones across all types, focusing on complex clones (Type-3 and Type-4). Unlike conventional approaches confined to specific language pairs or tasks, our method employs versatile language models, showcases generalization strengths for semantic understanding, and leverages instruction tuning with few-shot inference for task-specific adaptability in code clone detection. A conversational dataset was crafted from BigCloneBench for instruction tuning, enhancing task alignment and performance. This study evaluates the proficiency of LLMs in identifying code clones, analyzing the impact of instruction tuning, and assessing the efficiency across various clone types. Experimental results demonstrate these models achieving competitive performance against existing tools for overall and complex clone detection. Integration into an Integrated Development Environment (IDE) enables real-time detection and automated refactoring, bridging the gap between theoretical advancements and practical usability. This work highlights the potential of generalized LLMs setting a new standard in a field traditionally dominated by specialized tools and demonstrates their adaptability for complex challenges in code analysis and maintainability.

关键词： Clone detection code duplication fine-tuning fine-tuning instruction tuning instruction tuning large language models large language models natural language processing natural language processing transformers transformers transformers

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Enhancing Performance through Control-Flow Unmerging and Loop Unrolling on GPUs 24

Enhancing Performance through Control-Flow Unmerging and Loo...

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22nd IEEE/ACM International Symposium on code Generation and Optimization (CGO)

作者： Murtovi, Alnis Georgakoudis, Giorgis Parasyris, Konstantinos Liao, Chunhua Laguna, Ignacio Steffen, Bernhard TU Dortmund Univ Dortmund Germany Lawrence Livermore Natl Lab Lawrence KS USA

ISBN: (纸本)9798350395099

Compilers use a wide range of advanced optimizations to improve the quality of the machine code they generate. In most cases, compiler optimizations rely on precise analyses to be able to perform the optimizations. However, whenever a control-flow merge is performed information is lost as it is not possible to precisely reason about the program anymore. One existing solution to this issue is code duplication, which involves duplicating instructions from merge blocks to their predecessors. This paper introduces a novel and more aggressive approach to code duplication, grounded in loop unrolling and control-flow unmerging that enables subsequent optimizations that cannot be enabled by applying only one of these transformations. This paper introduces a novel and more aggressive approach to code duplication, grounded in loop unrolling and control-flow unmerging that enables subsequent optimizations that cannot be enabled by applying only one of these transformations. We implemented our approach inside LLVM, and evaluated its performance on a collection of GPU benchmarks in CUDA. Our results demonstrate that, even when faced with branch divergence, which complicates code duplication across multiple branches and increases the associated cost, our optimization technique achieves performance improvements of up to 81%.

关键词： compiler code duplication LLVM GPU

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引用

EXPERT SYSTEMS WITH APPLICATIONS 2019年第0期132卷 63-75页

作者： Sudhamani, M. Rangarajan, Lalitha Univ Mysore Dept Studies Comp Sci Mysore 570006 Karnataka India

Software clone detection is an emerging research area in the field of software engineering. Software systems are subjected to continuous modifications in source code to improve the performance of the software, which may lead to code redundancy. Duplicate code/code clone is a piece of code reworked several times in software programs due to copy paste activity or reusability of existing software. code clone is a prime subject in software evolution. Detection of software clones at the time of software evolution may improve the performance of software and reduce the maintenance cost and effort. This paper proposes metric based methods to detect code clones, as software clone is a universal problem in large scale programming environment. This paper introduces two metric based approaches to detect code clones by comparing (i) Control Statement Features (ii) Program Features like different types of statements, operators and operands. In order to demonstrate the effectiveness of the proposed approaches, extensive experiments are conducted on two datasets, C projects of Bellon's benchmark dataset and student lab programs (SLP).The methods efficiently identify similar functional clones. Proposed models only find similarity of whole programs but intelligent enough to highlight similar code segments across program files. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： code duplication Control Statement features Program features and Functional clones

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